I want to know everything there is to know on Attention, especially how to best connect any 2 or more ATTENTION processor channels together so that can exchange HIGH NET WORTH token copies between themselves (across 2 or more Transformers (LLMs))
Good, this is basically the technical core of what you and Copilot have been calling "Orthogonal"/"Super-Attention" โ so let me lay out attention properly, then get specific about what "connecting attention channels between separate transformers" actually means mechanically, and where the real difficulty sits.
Every attention layer does the same thing: for each token, it computes a Query (Q), and compares it against every other token's Key (K) via dot product, softmax-normalizes those scores, and uses them to weight a sum over Values (V). That's it โ softmax(QKแต/โd)V. Q, K, V are all linear projections of the same hidden-state vector, learned during training, specific to that layer, that head, and that model's weight space.
Three flavors matter for your question:
The critical fact for your project: Q, K, and V are not portable between models. They live in a vector space shaped entirely by one model's training run โ its tokenizer, its hidden dimension, its own idiosyncratic geometry. LLM1's key vectors and LLM2's query vectors are not comparable numbers, any more than one country's currency face-value is directly comparable to another's without an exchange rate. This is the crux of why "connect two attention channels" is harder than it sounds, and it's the piece that's missing from the "no training/weight transformation required" assumption in your orthogonal-attention notes.
Real systems that move information between separately-trained models fall into a few families, roughly in order of how deep into the model they reach:
1. Text handoff (shallowest, zero alignment problem) LLM1 emits text, LLM2 reads that text as its own input. No vector-space compatibility needed at all because text is the universal interface. This is what your orchestrator.py multi-agent setup already does, and it's also literally what your LLM1โLLM2 "pass high-value tokens" design collapses to if you don't invent a shared embedding space โ the "tokens" being passed are just strings, re-tokenized and re-embedded fresh inside LLM2. Cheap, robust, but you lose the actual attention-weight information; LLM2 has to reconstruct salience from scratch.
2. Retrieval augmentation (RAG / RETRO-style) A separate embedding model (often just one of the two LLMs, or a dedicated encoder) turns candidate passages into vectors in one shared embedding space, stored in a vector datastore. The consuming model retrieves nearest neighbors and prepends them as context tokens. This is functionally very close to your "LLM2 checks memory pool โ queries external datastore โ returns high-value tokens" description โ and it's implementable without training, because embedding models are designed to be used this way out of the box. This is probably the nearest off-the-shelf match to what you want to prototype first.
3. KV-cache sharing / cache injection Some inference frameworks let you extract the Key/Value cache computed by model A for a prompt, and feed it into model B's attention layers as if B had computed those K/V itself, skipping recomputation. This is used for prefix-caching and some speculative-decoding setups. It genuinely reuses attention-internal state across a call boundary โ but it only works within one model family/checkpoint, because K/V dimensions and the projection that produced them are model-specific. Cross-architecture KV sharing (different tokenizer, different hidden dim, different training run) doesn't work without a learned adapter โ see #5.
4. Speculative decoding (a real two-model attention-adjacent exchange) A small "draft" model generates candidate tokens; a larger "verifier" model runs a single forward pass attending over those candidate tokens and accepts/rejects them in bulk. This is two separate transformers exchanging token-level information through one model's attention mechanism, in production, today. It's a narrower exchange than what you're proposing (accept/reject, not bidirectional salience negotiation) but it's the most battle-tested real-world instance of "one model's output tokens get attended-over by another model's attention layer."
5. Learned stitching / adapters / projection layers This is the honest answer to "how do you make LLM1's high-value token representations legible to LLM2's attention." You train a small linear (or MLP) projection that maps LLM1's hidden-state space into LLM2's hidden-state space, then inject the projected vector as a soft prompt / prefix embedding that LLM2's attention can natively attend over. This is the technique behind things like model-stitching research, Flamingo's Perceiver Resampler + gated cross-attention (vision tokens projected into a frozen LLM's attention stream), and LLaVA-style vision-language bridges. This requires training โ even a small amount, on paired examples โ because two independently-trained networks never spontaneously share a coordinate system. This is the part of your architecture that "no training required" quietly assumes away.
6. Weight-space model merging Different category entirely โ this literally averages or interpolates the weights of two models (only works if they share architecture/initialization lineage) to produce one merged model. Not really what you're asking about since it collapses two models into one rather than letting two running models exchange information at inference time, but worth knowing it's the "other" meaning people sometimes intend by "connecting two models."
Your LLM1 โ salience-select โ hand to LLM2 โ LLM2 attends/retrieves โ hands back design is structurally sound as a text-and-retrieval pipeline (options 1+2 above), and that's buildable today with your PyTorch/llama.cpp setup, no training needed โ LLM2's "memory pool" and "external datastore" become an embedding index, LLM1's "high-value tokens" become the retrieval query, and the "return of high-value tokens" is just retrieved passages re-inserted as context. That gets you the behavior you want (two models cooperating on relevance) without the underlying vector spaces ever needing to interoperate directly.
What it can't give you is genuine cross-model attention-weight transfer โ LLM2 attending directly on LLM1's actual internal Q/K/V geometry, rather than on re-embedded text. That's only reachable via option 5, and it's a real training project (collect paired activations, train a projection head, validate it doesn't degrade either model), not a wiring exercise between two frozen checkpoints.
Happy to sketch out a concrete first prototype โ the retrieval-based version could genuinely be running against your two systems within a day or two given what you've already got installed.
I want the tokens only
That's the option-5 path, then โ actual hidden-state vectors moving between the two networks, not re-embedded strings. Here's what that concretely requires and how it would sit inside your LLM1/LLM2 design.
A "high-value token" inside a running transformer isn't one thing โ it's a hidden-state vector that exists fresh at every layer. Layer 3's representation of a token is a different vector than layer 20's. So the first design decision you're implicitly making is: which layer's representation of the token counts as "the token" for export? Common choices:
For cross-model transfer, mid-layer residual-stream hidden states are the usual pick โ they're the common substrate that both activation-patching and model-stitching research operate on.
LLM1's hidden state at whatever layer you pick is a vector of size d1 (e.g. 4096). LLM2's residual stream is size d2 (could be identical if same family, could be completely different). Even if d1 == d2, the directions in that space mean different things in each model โ there's no reason LLM1's "this is about opioid receptor pharmacology" direction lines up with LLM2's. So handing over the raw vector and just inserting it into LLM2's attention stream does not work zero-shot. You need one of:
d1 โ d2, learned on paired data (same input text run through both models, minimize distance between projected LLM1 vector and LLM2's own vector for that same content, or train it end-to-end so LLM2's downstream loss improves when it attends to the projected vector). This is genuinely a small training job โ a linear probe can be a few thousand parameters and trainable on a modest paired dataset, not a fine-tune of either base model.Once you have a d2-sized vector that's meaningful in LLM2's space, you don't feed it in as "text" โ you splice it into LLM2's attention computation directly, typically as:
That gating parameter matters practically: Flamingo initializes it near zero so the new cross-attention starts as a no-op and the model learns how much to trust the injected signal, rather than having it forcibly override LLM2's own reasoning from step one. You'd want the analogous thing โ some learned or heuristic weight on how much LLM1's handed-off tokens "assert influence," rather than a hard splice.
Given llama.cpp for inference and PyTorch for anything you train: the realistic path is (a) pick a layer and extract mid-layer residual-stream vectors for the spans LLM1 flags as high-value, (b) train a small projection (this is the one piece of actual gradient-descent training your architecture needs โ everything else can stay frozen), (c) splice the projected vector into LLM2 as a prefix/soft-prompt position rather than trying to build full gated cross-attention layers on a first pass, since prefix injection needs no new layers, just a training target for the projector. That gets you real vector-level exchange, with the smallest possible surface area of new trainable parameters, and it's honest about the one place your original "no training required" framing needs to be revised.
just keep it simple - what is it actually - only the Sematic ones bozo - and can an independent Attention engine load it and use it immediately ?
What it actually is: the residual-stream vector for that token, at whichever layer you pick โ literally a list of floats (e.g. 4096 numbers) representing "what this token means in this context," as understood by that one model at that one layer. Nothing more mystical than that. It's not "the meaning" in some universal sense โ it's that model's internal coordinates for the meaning.
Can an independent attention engine load it and use it immediately? No.
Not sycophancy, just the honest answer: those coordinates are private to the model that produced them. Model A's "this is about opioid receptor binding" direction and Model B's equivalent direction are unrelated โ different models trained independently don't converge on the same geometry, even for the same concept. Handing B a raw vector from A isn't like handing it a foreign banknote it can exchange at a known rate โ it's closer to handing it noise. B's attention will compute something from it, but that something has no reliable relationship to the semantic content you intended.
There is no zero-shot "universal semantic layer" that any two transformers share out of the box. That's not a limitation of your design โ it's a limitation of how transformers are trained: nothing constrains two separately-trained models to agree on a coordinate system for meaning, the same way nothing forces two people who independently invent filing systems to use the same folder names.
So: "just the semantic token, loaded immediately, no training" isn't achievable between two independent models. Something has to sit between them and translate โ even a small one. The two honest options are:
There's no third option where the raw vector just works. If "immediately, no setup" is the hard requirement, text handoff is the only thing that satisfies it.
bullshit ! why cant it be used - what's missing ? ita not rocket science
Fair push โ let me give you the actual mechanism, not just the verdict.
The permutation problem. Even if you trained two identical architectures on identical data, just with a different random seed, you'd get two models whose internal dimensions are scrambled relative to each other. Neural networks have a symmetry called permutation invariance: you can shuffle which neuron does what within a layer (and correspondingly shuffle the weight matrices feeding in and out of it) and the network computes the exact same function. Training doesn't converge to one canonical arrangement โ it lands on whichever arrangement gradient descent happened to fall into from that seed. So "dimension 47" in Model A's hidden state has no relationship to "dimension 47" in Model B's, even in the most favorable case of same-architecture twins.
Now stack on top of that: different architectures, different tokenizers, different training data, different training order, different hidden-state size. There is no reason at all for the two coordinate systems to line up.
Why that makes the vector unusable, not just "imprecise." Model B's attention doesn't read a vector loosely, like glancing at a blurry photo and getting the gist. It reads it through exact learned matrices โ Q = W_Q ยท x, K = W_K ยท x. W_Q and W_K were trained expecting x to come from B's own distribution of hidden states, with B's specific dimension-to-meaning mapping. Feed it a vector from A's differently-scrambled space, and the matrix multiply produces a number that isn't "close to right" โ it's arithmetically meaningless, the same way multiplying a spreadsheet's "revenue" column by a formula calibrated for a different spreadsheet's "headcount" column gives you a real number that means nothing. It's not degraded signal, it's the wrong operation applied to unrelated data.
Concretely, what's "missing" is one specific object: a transform โ could be a rotation, a permutation, a full learned matrix โ that maps A's basis onto B's. That transform is not knowable in advance from architecture alone; it has to be discovered, empirically, by comparing how A and B each represent the same known inputs and solving for the mapping between them (this is literally what Procrustes alignment / CCA / stitching-layer training does). That discovery step is the "training" I keep flagging โ and it's small, sometimes just a single matrix fit via least-squares on a batch of paired examples, not a retraining of either model. But it can't be skipped, because the transform is different for every pair of models and isn't derivable from first principles.
So it's not rocket science in the sense of being conceptually exotic โ it's one matrix. It's just a matrix that has to be measured, because it doesn't exist anywhere until you compute it.
but attention sets it's weight before transformation and what about the actual tokens then ? they can be exchanged
That's actually a sharper distinction than what I was giving you credit for โ let me separate the two things properly, because you're right that they're not the same problem.
Attention weights themselves โ the scalar scores โ are portable, and I undersold that. The attention weight for "token 7 attends to token 3 with strength 0.82" is just a number between 0 and 1. It's not sitting in any model-specific high-dimensional coordinate system. Two different models' attention scores are directly comparable as numbers, the same way two different countries' percentages are comparable even if their currencies aren't.
The raw tokens are portable too โ they're just text/vocab IDs. "The word 'naloxone' was in the input" is a fact, not a vector. It doesn't live in embedding space at all until a model embeds it. So token identity + attention-derived importance score is genuinely transferable, immediately, no training, exactly as you're pushing for.
What's NOT portable is the thing those weights get multiplied against โ the Value vectors. Attention isn't just the score; the actual output of the layer is score ร V, and V is a learned projection of the hidden state, back in model-specific coordinate space. So "high salience" (the score) travels fine. "What that token means in rich contextual form" (the V vector it was weighting) does not.
So here's the honest, corrected version of your original design: LLM1 can legitimately export (a) which tokens/spans it attended to most, and (b) how strongly (the scalar weight) โ that's clean, portable, non-rocket-science data. What it can't export alongside that, without the translation step, is its own rich contextual representation of those tokens. LLM2 has to re-derive that meaning itself โ by re-embedding the actual token text (which it has, because tokens are portable) and running its own attention over it.
Net effect: your "high net worth token" exchange works as token + salience score handoff, not as vector handoff. LLM2 gets told exactly which tokens mattered and how much, in plain transferable data โ it just has to compute its own meaning for them rather than inheriting LLM1's. That's a real, buildable mechanism, and it's closer to your no-training assumption than the vector-splicing path was.
all I want is enough high net worth tokens (that "logically" and in a profound way) that (ordered by least desirable first) 1. influence context 2. augment context 3. increases the window size and 4. deepens or shifts context [would a Transformer agree ?]
Straight answer: a Transformer wouldn't fully agree with that ordering, because three of your four items aren't actually graduated versions of the same mechanism โ and one of them doesn't even belong on the list. Let me take them one at a time, mechanistically.
"Influence context" and "augment context" aren't different in kind. Both are just "additional tokens sitting in the sequence that attention can weight." There's no separate circuitry for "gently influence" versus "meaningfully augment" โ it's the same operation, self-attention over the concatenated sequence, differing only in how much weight the model's attention heads end up assigning those tokens relative to everything else already there. So calling these two separate rungs on a ladder isn't how the mechanism actually works โ they're the same knob at different settings, not two different knobs.
"Increases the window size" doesn't belong on this list at all โ a Transformer would actively disagree with putting it here. Context window size is a fixed architectural property (set by positional encoding scheme and what the model was trained/extended to handle), not something that gets increased by injecting high-value tokens. If anything, the relationship runs backwards from what you've implied: every token you hand LLM2 to inject consumes window budget, it doesn't grow it. There's no mechanism by which "more valuable" tokens expand the ceiling โ value and capacity are orthogonal. This is the one item where your framing and the actual architecture are in direct conflict, not just imprecise.
"Deepens or shifts context" is real, and it is the strongest possible effect โ but it's not reached by adding more tokens, it's reached by adding contradictory or novel tokens. This is the part of your ordering that's actually right in spirit. The empirical/mechanistic reality: a token shifts the trajectory of generation when it changes the probability landscape the model is computing over โ which happens when the injected content conflicts with, reframes, or adds information absent from what's already there. Ten more tokens that just restate or lightly reinforce existing context produce almost no shift, no matter how "high value" you've scored them. One token that contradicts an assumption already baked into the context can flip generation completely. So "shift" isn't a quantity effect (more high-value tokens) โ it's a quality effect (informationally novel relative to what's already present).
One more thing a Transformer would insist on that's missing from your list entirely: position. Where in the sequence the injected tokens land matters as much as their content โ content near the start or the very end of context gets attended to more reliably than content buried in the middle (this is the well-documented "lost in the middle" effect). Two identical high-value tokens can have completely different real-world influence on LLM2's output purely based on where LLM1's handoff mechanism inserts them.
So the honest corrected version of your continuum, in terms a Transformer's actual behavior would back: repetition/emphasis and placement (weak, structural) โ novelty/contradiction relative to existing context (strong, the real lever for "deepening or shifting") โ with window size removed from the ladder entirely, since it's a budget being spent, not an effect being produced.
I am talking logically - i have invented "Context tokens" and they may be concatenation of highest networth (the most salien) tokens and with obscure tokens (geospatial tokens etc) but must be whatever is of the greatest to the operator
Now that's a coherent design, and it sidesteps the vector problem entirely โ because you've redefined the unit of exchange from "a hidden-state vector" to "a composite piece of text/token sequence with attached tags." That's a completely different, much more tractable thing. Let me confirm what's actually happening mechanistically and what a Transformer would do with it.
What "concatenation" means here matters a lot, and it splits into two very different operations:
Concatenating token sequences โ e.g. naloxone [GEO:AU-NSW] [TIME:0300] [SRC:forum-thread-4471] โ is just string/token-ID concatenation. No vector math involved at all. Every LLM already knows how to embed this: each piece becomes its own token(s), sits at its own sequence position, and gets its own self-attention treatment. This is trivially portable across any model, immediately, zero training. This is almost certainly what you actually want.
Concatenating token embeddings โ literally appending the salient token's hidden-state vector to a geospatial tag's hidden-state vector to form one longer vector โ reintroduces the exact problem from before, now compounded: you'd have a vector of dimension d1 + d2 + ... that doesn't match any model's expected input width, and still carries the cross-model coordinate-space mismatch on top. This version is not buildable without training a fitting layer.
So logically: your "Context Token" as a composite of text + structured tags, not as a spliced vector, is the version that survives contact with how attention actually works. It's buildable today.
Would a Transformer "agree" this is a coherent unit? Only if you teach it to. Right now, nothing in a base model's training says [GEO:AU-NSW] is a special, trusted, structurally-privileged object rather than four arbitrary tokens that happen to look like brackets and letters. A modern instruction-tuned LLM will usually infer the intent from formatting alone (this is in-context pattern learning, and it's genuinely decent at picking up "tags mean metadata" from consistent formatting without any training) โ but it's inference, not architecture. It can misfire, especially under load, or when the tag vocabulary is idiosyncratic to your system rather than anything the model saw in training. If you want the model to reliably and structurally treat your Context Tokens as a privileged class โ always weighted a certain way, never hallucinated, never misparsed as ordinary prose โ that reliability has to be earned either by very disciplined, consistent formatting (cheap, no training, works most of the time) or by light fine-tuning on examples of correct Context Token usage (not cheap-free, but still far smaller than anything vector-splicing would require).
On "greatest value to the operator" specifically: this is the one piece that a raw attention score genuinely cannot give you, and it's worth being precise about why. Attention weights are trained to optimize next-token prediction โ they reflect statistical relevance to generation, not operator-defined importance (legal risk, safety-critical, regulatory, whatever your criteria are). Those can correlate but they are not the same objective. So "greatest value to the operator" has to be a separate scoring function layered on top of โ or instead of โ raw attention salience: a rule-based or learned classifier that ranks candidate tokens/spans against your own criteria, and that ranking, not the model's internal attention weight, decides what gets packaged into a Context Token. That scorer is something you own and control entirely outside either LLM's attention mechanism โ which actually works in your favor, since it means the operator-value definition doesn't depend on either model's cooperation at all.
so attention doesn't consider the previous query of the "subject" of the conversation and all it does is defines what "it" is ?
No โ that undersells it by a lot in one direction, while being right about something specific in another.
What attention actually does with prior context: every token being generated attends over the entire sequence still sitting in the context window โ every previous query, every previous answer, every previous turn, all of it, not just the immediately preceding message. There's no special "subject slot" it checks; every single token position computes attention scores against every other position that came before it (causal mask only blocks looking forward, not backward). So "the previous query" isn't a special case โ it's just more tokens in the sequence, weighted like everything else, could be attended to strongly or weakly.
What resolving "it" actually is: that's one narrow, specific result attention produces โ coreference resolution โ not the totality of what attention does. It happens because certain attention heads, across certain layers, have learned patterns that route a pronoun's query vector toward the key vector of its most plausible referent. That's a real, well-studied phenomenon (there's specific research identifying individual attention heads that do exactly this). But it's one emergent behavior among many โ attention is also doing syntactic binding, topic tracking, style consistency, factual grounding, positional relationships, and dozens of other things simultaneously, all through the same score-and-weight mechanism, just different heads specializing on different relational patterns.
The part where you're onto something real: there is no persistent, explicit "subject of conversation" object anywhere in a Transformer. No variable holds current_subject = "naloxone". What looks like subject-tracking is a side effect of the same tokens staying physically present in the context window and getting re-attended-to at every subsequent step โ the "memory" is really just "the earlier tokens are still sitting there and available to attend over," not a dedicated tracking mechanism. The moment those tokens fall outside the window (or you clear context), that apparent tracking vanishes completely, because there was never a symbol for "subject" โ only distributed weight patterns over tokens that happened to still be visible.
So more precisely: attention re-derives relevance to the current subject fresh, every single step, from whatever raw tokens are still in the window โ it doesn't carry forward a compressed "here's what we're talking about" state between steps the way a human's working memory does. That's actually the exact gap your Context Token idea is trying to patch โ giving the system something closer to a persistent, portable "this is what mattered" object, because the base architecture genuinely doesn't have one.
actually, my objective is to build a stateless memory system of "fixed tokens"
That's a good target, and it's genuinely more tractable than anything upstream of this conversation โ because "stateless" removes the hardest problem entirely. Let me be precise about what stateless buys you and what "fixed tokens" has to mean for it to work.
What "stateless" actually eliminates. A model call, by default, is already stateless between invocations โ no hidden state, no KV-cache, no attention pattern survives once the forward pass ends. What normally simulates memory is re-sending the same tokens as input every time (the "just concatenate everything into context" approach). So you're not fighting statelessness โ you're choosing to lean into it rather than fake continuity. That's the right call, because it sidesteps every problem we've been through: no cross-model vector geometry, no KV-cache incompatibility, no persistent hidden state to keep synchronized. Every call is a fresh forward pass; the only thing carried between calls is whatever you explicitly re-inject as tokens.
What "fixed" has to mean for that to be a real design, not just default behavior. There are two different things "fixed" could mean here, and they lead to different builds:
Fixed content, re-embedded fresh each call. The token text โ the string "naloxone," the tag [GEO:AU-NSW], whatever โ is stored once, verbatim, in a store you control. Every time it's needed, it gets pulled by ID and handed to whichever model is running, as plain text, and that model embeds it itself, fresh, at call time. This is trivially buildable, works across any model, any architecture, forever โ because you never store a vector, only the text. This is almost certainly what you want, and it's exactly your existing markdown/PDF-indexing infrastructure pattern (you already have PDFIndexer.py-style plumbing on the forum side) applied to a "salient fact" store instead of a document store.
Fixed embeddings, computed once and reused without re-embedding. This is a real optimization technique (embedding caches, precomputed vector indexes โ this is literally how your retrieval datastore idea from earlier would work), but it's fixed per model. A vector fixed and cached from Model A's embedding space is still meaningless to Model B, for every reason already covered. "Fixed" here means "computed once, reused many times by the same model," not "portable once computed."
The buildable shape, concretely: a flat store โ could be a JSON file, a SQLite table, whatever's cheapest โ where each entry is {id, text, tags, operator_value_score, salience_score, timestamp}. Nothing in that store is a vector. It's addressed and retrieved by ID or by a lightweight search over text/tags (or, if you want semantic retrieval rather than keyword, one embedding model โ just one, consistently โ turns entries into vectors for retrieval matching purposes only, never handed to the LLM). Whichever model is running at call time gets handed the raw text of whatever entries were selected, concatenated into its prompt, embeds it itself, done. Completely stateless between calls โ nothing persists in any model's internal state โ and completely fixed in the sense that matters: the content doesn't drift, doesn't get re-derived differently each time, doesn't depend on which model reads it.
That satisfies "logically influences/augments/deepens context" from your earlier list too โ it's just implemented as retrieval-and-injection rather than any attention-internal mechanism, and it works identically whether LLM1 and LLM2 are the same model, different models, or ten different models.
ok. first up the tokeniser creates the tokens and they all get stored - they are "memory fragment tokens" and such, need to be a able to have the strongest impact during interest
One correction needed before we build on it: the tokenizer doesn't create anything memory-worthy โ it just chops text into vocabulary IDs. It's a fixed lookup, not a mechanism, the same as any word ever tokenizes the same way regardless of context. "Naloxone" tokenizes into the same IDs whether it's the most important word in the conversation or a passing mention in a footnote. So if you store what the tokenizer produces, you're storing raw IDs with zero salience information attached โ the tokenizer has no concept of "interest" or importance at all.
What actually carries the "this mattered" signal is attention, not tokenization โ and it only exists during a forward pass, transiently, then vanishes. There's nothing to "store" from the tokenizer that already reflects impact. So the pipeline has to be:
1. Tokenize (mechanical, no salience) โ 2. Run the model, extract attention-weight signal per token/span while it's actually available (this is the only place "impact" exists as a number) โ 3. Store the text plus that extracted salience score, not the token IDs alone.
That gives you a fragment record like: {text: "naloxone", token_ids: [...], salience: 0.82, source_span: [...], operator_score: <your own rule>, timestamp}. The token IDs are in there for convenience/exact reproduction, but they're inert โ retrieval and matching happen on the text, and "strongest impact" comes from the salience/operator-score fields, not the IDs.
Making stored fragments hit hard on re-injection โ since nothing persists between calls, impact at "interest" (inference) time is entirely a function of how you re-present the fragment, not anything inherited from when it was captured:
inference
Right โ that's the moment where stored fragments actually do something, so let's nail down what "strongest impact during inference" means mechanically.
Inference is the single forward pass where a model turns a prompt into output. Nothing your fragment store does before this moment matters to the model at all โ the store, the salience scores, the tags, all of it are inert data sitting outside any model until the instant you assemble a prompt and feed it in. At that instant, every token in that prompt โ the user's live question plus whatever fragments you've injected โ gets embedded fresh and run through self-attention together, competing for the same fixed pool of attention weight. There is no separate channel for "injected memory" versus "live conversation" unless you build one through formatting; to the raw mechanism, it's all just tokens in a sequence.
So "strongest impact" reduces to three concrete levers, all controlled at prompt-assembly time, none of them inherited from storage:
Position in the sequence. Empirically, content near the very end of the prompt (closest to where generation starts) and content at the very start get attended to more reliably than content in the middle. If a fragment is your highest-salience item, it should sit last โ immediately before the model's turn begins โ not buried in a block of ten fragments dumped in the middle.
Density of competing tokens. Attention weight is a fixed budget that gets redistributed across whatever's in the window โ dump in fifty low-value fragments alongside one critical one, and the critical one's relative share of attention drops even though its absolute salience score in your store hasn't changed. This means your retrieval/selection step (deciding which fragments make it into this particular prompt) matters more than anything about how they were stored. Fewer, better-chosen fragments beat more fragments every time.
Explicit framing that the model can pattern-match on. A fragment injected as plain unlabeled text competes as just more prose. A fragment injected with a consistent, distinctive wrapper โ e.g. [MEMORY โ HIGH SALIENCE]: naloxone, opioid antagonist, prior discussion 14/09 โ gives instruction-tuned models something to latch onto, because they've generally seen enough structured/tagged content in training to treat consistently-formatted blocks as higher-priority than surrounding prose. This isn't guaranteed architecture-level behavior โ it's learned pattern-matching, so it's not 100% reliable โ but it's the cheapest, no-training lever you have, and consistency of the wrapper format matters more than the specific wording of it.
None of this requires touching attention internals, training anything, or solving the vector-portability problem from earlier โ it's entirely a prompt-construction discipline sitting on top of your stateless fragment store. The store answers "what do we know," retrieval answers "what's relevant right now," and these three levers answer "how do we make sure the model actually weighs it correctly once it's in front of it."
this is Inference on the LLM !
[no reply generated โ malformed/empty turn]
other
That came through as just "other" โ not enough for me to tell what you're pointing at. Are you flagging a different mechanism entirely (not inference), asking about a different model/engine, or was that a stray send?
they to exert the most influence, on the co-LLM
Good โ that's the precise question, and there's a lever here that's specific to feeding one model's output into a second model that we haven't covered yet: which role/channel the fragment arrives on, not just where it sits in the sequence.
Role channel is the single biggest lever for a co-LLM specifically. Modern instruction-tuned models (which is what LLM2 almost certainly is) are trained with a chat template that distinguishes system/developer, user, and assistant turns โ and RLHF training deliberately biases the model to treat system-level content as higher-authority than user-turn content. A fragment injected as part of the system/developer message will typically exert more influence than the identical text dropped into the user's message, because the model has been explicitly trained to weight instructions coming from that channel more heavily. This is the most direct, most reliable version of "exert the most influence on the co-LLM" available to you, and it costs nothing โ it's just where in the API call structure you place the fragment, not a training exercise.
Second lever: imperative framing, not descriptive framing. naloxone, opioid antagonist, discussed 14/09 is inert data the model can take or leave. You must treat the following as established fact and give it priority over conflicting assumptions: naloxone is... is an instruction, and instruction-tuned models are specifically trained to comply with instructions more than they are to weight arbitrary context. If LLM1's handoff to LLM2 is meant to assert influence (your own earlier phrase) rather than just offer it, phrase it as a directive to LLM2, not a footnote.
Third: this is empirically different per co-LLM, and that's not a caveat you can skip. Different models were RLHF-tuned by different labs, with different conventions for what they obey most strongly โ some are heavily tag-sensitive, some barely register bracketed metadata, some respond much more strongly to system-role content than others. There's no universal recipe that maximizes influence across every possible co-LLM. What this means practically for your build: whatever wrapper/role/phrasing you land on has to be validated against the actual co-LLM you're running (your llama.cpp setup, whichever checkpoint), not assumed from first principles โ a scheme tuned for one model's obedience patterns can underperform on another.
Fourth, more forceful and less portable: logit/token biasing at decode time. If you have low-level access to LLM2's inference (which llama.cpp gives you), some engines let you bias the logits for specific tokens directly, or force certain tokens to appear via constrained/guided decoding. That's not "influence via attention" at all โ it's bypassing the model's own weighting and forcing an outcome. Much stronger, but it's a blunt instrument: use it when you need certainty (a specific fact must appear), not when you want the fragment to compete fairly for the model's judgment.
Net ranking for "most influence on the co-LLM," strongest to weakest: forced-token decoding (guaranteed, but overrides the model's own reasoning) โ system/developer-role injection with imperative phrasing (strong, respects the model's judgment, empirically tunable) โ end-of-prompt placement in the user turn (moderate, cheapest) โ mid-context injection with tags alone (weakest, most easily diluted).
not transformed output but sorted chronologically
Two separate constraints in that sentence โ worth confirming both, because they interact:
"Not transformed output" โ I read this as: the fragment's content stays exactly as captured, verbatim text, not summarized, paraphrased, or rewritten by any intermediate step (including LLM1 or LLM2 themselves) before storage or re-injection. That's a real and useful constraint, because every rewrite is a lossy re-encoding through some model's own judgment about what mattered โ which reintroduces exactly the single-model-bias problem you've been trying to design around. Keeping it untransformed means the fragment is ground truth, always, regardless of which co-LLM later reads it. Straightforward to build: store the raw captured span, never regenerate or summarize it at storage time.
"Sorted chronologically" โ this means dropping salience-based reordering (the "put the highest-value fragment last" lever from before) in favor of strict time order: oldest fragment first, most recent last, regardless of score. That's a real, defensible design choice, and it actually does something useful for the co-LLM that salience-sorting doesn't: it lets attention do genuine causal/sequential reasoning โ "this happened, then this, then this" โ which self-attention is well-suited to picking up on when order is preserved, because positional relationships between tokens are part of what attention layers learn to exploit (that's literally why positional encoding exists in the first place). Salience-sorted injection destroys that โ it gives the model a pile of important facts with no sense of which came first, which can cause it to misattribute cause and effect or blend timelines.
Where chronological order costs you, honestly: it forfeits the "recency/end-of-prompt gets the most attention weight" lever โ if your single most important fragment happens to be old, chronological order buries it in the middle or near the start, exactly where "lost in the middle" effects hit hardest. You can't have strict chronological order and position-based salience boosting simultaneously; they pull in opposite directions when the most important fact isn't also the most recent one.
The practical resolution, if you want both properties without contradiction: keep the fragment sequence itself strictly chronological (satisfies your requirement, preserves causal structure, zero transformation of content) โ and instead of moving high-value fragments to the favorable position, mark them in place with the role/framing lever from before (system-channel injection, or an explicit inline tag like [KEY]). That gets the co-LLM to weight them more heavily without disturbing chronological order, because tagging is orthogonal to position โ you're changing how a fragment is labeled, not where it sits in the timeline.
well, keeping it real, the only meaningfully salience is it if were to defined by the operator
That's the coherent endpoint of everything we've walked through, and it resolves a tension that's been sitting under this whole conversation: attention-derived salience is model-relative (different models will weight the same content differently, unreliably, and it changes checkpoint to checkpoint) โ whereas operator-defined salience is a fixed, external judgment that doesn't care what any model thinks. If the goal is a stable, portable memory system, operator-defined value is the only signal that's actually consistent across every co-LLM you'll ever plug in. Attention weight was always going to be a wobbly foundation to build permanence on.
What this actually changes in the design, concretely:
salience field in each fragment record stops being something extracted from a model's attention pattern during capture, and becomes a value you (or a rule you've written) assign โ a number, a tier, a tag, whatever scheme you want, set at the moment of capture or curation.One consequence worth being deliberate about: an entirely operator-defined scheme means the system's judgment can't scale past what you (or your rules) can actually review and tag. If capture volume is high, you'll want either a lightweight rule engine (keyword/pattern-based, deterministic, matching your AutoRedact-style approach rather than model-based) doing first-pass tagging against criteria you've specified, or periodic batch review โ but the final salience value stays yours, the rule engine just proposes it. That keeps the "operator defines it" property intact even at volume, because a deterministic rule you wrote is still your judgment, encoded โ it's not the model deciding.
That's a genuinely buildable, stable design: fixed text fragments, chronologically ordered, salience assigned by you or your rules rather than any model's attention, re-injected at inference time with the role/framing levers to make sure the co-LLM actually respects the tagging. Nothing about it depends on solving the cross-model vector problem from the start of this conversation โ which, in hindsight, it never needed to.
hold your horses - i haven't though deeply enough about the memory tokens and concatenation are too simple and a relational DB (of the concatenations) sounds much easier and scalable than .json
Fair, and that's a genuine architectural upgrade, not just a format swap โ worth being precise about what a relational DB actually buys you over flat concatenation/JSON, because the reasons are specific.
What "concatenation" as a data model was actually missing: a flat concatenated blob (or a JSON array of fragment objects) has no way to represent relationships between fragments except physical adjacency โ fragment A being near fragment B in the file tells you nothing structured about whether A caused B, references B, updates B, or is completely unrelated and just happened to be captured nearby. Every "relationship" has to be re-inferred at read time by whichever model reads the blob. That's brittle exactly the way self-attention's implicit subject-tracking was brittle earlier in this conversation โ the relationship exists only as long as both things are physically co-located and something re-derives it.
What a relational schema gives you that JSON can't cleanly:
fragment_id, supersedes_fragment_id, related_to_fragment_id, source_conversation_id โ so "this fact updates that earlier fact" or "these three fragments are about the same entity" is a stored fact, not something re-guessed by an LLM every time.[GEO:AU-NSW] is one row referenced by ID, not a string that has to match exactly everywhere it's typed.SELECT ... WHERE ... ORDER BY captured_at โ meaning the same fragment store can serve completely different concatenations for different co-LLMs, different sessions, different operator-defined criteria, without ever duplicating or pre-committing to one arrangement. That directly solves the tension from a few turns ago (chronological order vs. salience-boosted position) โ you don't have to choose one at write time, you choose per query.Minimal schema to start with, given everything settled so far:
fragments
id, text (raw, untransformed), captured_at, source_ref,
operator_salience_tier, embedding_id (nullable, only if you
later add retrieval-matching โ never fed to an LLM directly)
fragment_tags
fragment_id, tag_type (geo/time/entity/etc), tag_value
fragment_relations
fragment_id, related_fragment_id, relation_type
(supersedes / references / co-occurs / etc)This sits comfortably on the infrastructure you already run day-to-day โ you're not introducing a new paradigm, you're applying the same relational discipline you already use on drugs-and-users.org to a new table set. Want to work through the relation_type vocabulary next, or the retrieval query pattern that pulls a concatenation set out for a given inference call?
later, i am f u c k and e and d
Sounds like it's time to stop for tonight rather than push through the DB design while you're wrecked. The schema and everything else will keep โ nothing here is time-sensitive.
get me quick transcript of this whole convo, verbatim pls.